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- Aghakhanian, F, et al.
(författare)
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INTEGRATION OF GWAS AND EPIGENETIC STUDIES IDENTIFIES NOVEL GENES THAT ALTER EXPRESSION IN THE MINOR SALIVARY GLAND IN SJOGREN'S DISEASE
- 2022
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 81, s. 72-73
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Sjogren’s disease (SjD) is an autoimmune disease characterized by reduced function of exocrine glands (i.e., salivary and lacrimal glands). Epithelial cell damage resulting from lymphocytic infiltration has been implicated in SjD etiology [1]. How genetic and epigenetic changes influence epithelial-immune cell interactions in SjD pathogenesis remain understudied.ObjectivesEvaluate the role of SjD risk loci in salivary gland tissue to gain insights into the potential genes involved in salivary gland dysfunction.MethodsSNPs from 16 regions with SNP-SjD associations (P<5x10-8) in our GWAS study (3232 SjD cases) and meta-analysis of ImmunoChip data (619 SjD cases) [2] were interrogated for eQTLs using Genotype-Tissue Expression (GTEx) minor salivary gland data. Subsequent analysis identified genes that were both eQTLs in the minor salivary gland and significantly expressed in RNA-seq and ATAC-seq data from the submaxillary salivary gland epithelial cell line, A253. Pathway enrichment analysis was performed using gProfiler on the genes where coalescence of eQTL, RNA-seq, and ATAC-seq data was observed. To further validate the results, we performed transcriptome-wide association study (TWAS) analysis using GWAS summary statistics and minor salivary gland eQTL GTEx data.ResultsIn total, 5884 genome-wide significant SNPs from 16 SjD risk loci were identified as potential minor salivary gland eQTLs using two discovery thresholds: p(FDR)<0.05 provided by eQTL study (3566 SNPs) and p(FDR)>0.05 and p<0.05 in eQTL study (2318 SNPs). Further analysis revealed 10 SjD risk loci with SNPs that were minor salivary gland eQTLs for a total of 155 unique genes that had a coalescence of RNA- and ATAC-seq data in A253 cells. Many SNPs altered the expression of the nearest gene to the risk allele (i.e., index gene), such as IRF5 and TNPO3 on chromosome 7 at 128Mb; however, this locus had 12 additional genes that were eQTLs in minor salivary gland. In contrast, other loci had no reported eQTLs for the index gene, but several reported eQTLs for other genes, such TYK2 on chromosome 19 at 10Mb that showed no change in TYK2 expression but eQTLs for 8 distant genes, including ICAM1. Pathway enrichment analysis revealed an enrichment in Butyrophilin (BTN) family interactions (R-HSA-8851) (PAdj=1.564x10-5), including the BTN2A1, BTN2A2, BTN3A1, BTN3A2 and BTN3A3 gene cluster in the MHC region. In further support, TWAS of the minor salivary gland and the SjD GWAS summary statistics (after Bonferroni correction) showed association between SjD and BTN3A2 (p=1.24x10-42), as well as many other loci in the MHC region. In addition, several long non-coding (lnc) RNAs on chromosome 17 were significant, peaking at RP11-259G18.1 (p=4.43x10-10).ConclusionThis study shows that SjD-associated risk alleles influence disease by altering gene expression in immune cells and minor salivary glands. Further, our analysis suggests that altered gene expression in the minor salivary gland expands beyond effects on the index gene to several genes on each locus. Interestingly, we observed minor salivary gland eQTLs for several BTN family genes, which act as cell-surface binding partners to regulate cell-cell interactions, including interactions between epithelial cells and activated T cells [3]. Future work will assess chromatin-chromatin-interactions within the 10 SjD risk loci in salivary gland cells and tissues to map local chromatin regulatory networks that regulate gene expression. Additional transcriptional studies of SjD minor salivary gland tissues will provide further insights into how altered gene expression in the salivary gland influences SjD pathology.References[1]Verstappen. Nat Rev Rheumatol 2021;17(6):333-348.[2]Khatri, et al. Annals of Rheumatic Diseases 2020;79:30-31.[3]Arnett HA, Viney JL. Nature Reviews Immunology 2014;14:559-569.Disclosure of InterestsFarhang Aghakhanian: None declared, Mandi M Wiley: None declared, Bhuwan Khatri: None declared, Kandice L Tessneer: None declared, Astrid Rasmussen: None declared, Simon J. Bowman Consultant of: Abbvie, Galapagos, and Novartis in 2020-2021., Lida Radfar: None declared, Roald Omdal: None declared, Marie Wahren-Herlenius: None declared, Blake M Warner: None declared, Torsten Witte: None declared, Roland Jonsson: None declared, Maureen Rischmueller: None declared, Patrick M Gaffney: None declared, Judith A. James: None declared, Lars Ronnblom: None declared, R Hal Scofield: None declared, Xavier Mariette: None declared, Marta Alarcon-Riquelme: None declared, Wan Fai Ng: None declared, Kathy Sivils Employee of: Current employee of Janssen, Gunnel Nordmark: None declared, Umesh Deshmukh: None declared, A Darise Farris: None declared, Christopher Lessard: None declared
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- Wiley, MM, et al.
(författare)
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FUNCTIONAL EVALUATION OF THE SJOGREN'S SYNDROME AND SYSTEMIC LUPUS ERYTHEMATOSUS DDX6-CXCR5 RISK INTERVAL
- 2020
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 79, s. 89-90
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Sjögren’s Syndrome (SS) and Systemic Lupus Erythematosus (SLE) are distinct chronic, complex autoimmune diseases with shared characteristics such as autoantibodies, heightened interferons, and polyarthritis. SS and SLE genome-wide association studies (GWAS) report strong associations with theDDX6-CXCR5risk interval. DDX6 suppresses interferon stimulated gene expression and CXCR5 regulates T cell functions implicated in autoimmunity.Objectives:To identify functional variants that impact regulation in theDDX6-CXCR5interval.Methods:Fine-mapping was done using ImmunoChip data from 3785 SLE, 1916 SS cases and 6893 population controls of European ancestry that were imputed and tested for SNP-trait association. Bayesian statistics assigned posterior probabilities to SNPs and defined a credible set of risk variants. Bioinformatic analyses further prioritized variants with predicted functionality. Electrophoretic mobility shift assays (EMSAs) and luciferase expression were used to validate predicted SNPs in EBV transformed B (EBV B) cells.Results:While some differences were observed, the overall SS and SLE association signals were similar. SNP-SS rs9736016 nearCXCR5and SNP-SLE rs76409436 nearDDX6were the most significant but did not show evidence of functionality. Bayesian statistics defined credible sets of variants in strong D’ in common between both SS and SLE. Bioinformatics analyses (Haploreg, RegulomeDB, ENCODE data, etc) further refined the credible set and identified 5 common SNPs with strong evidence of functionality in immune cell types: rs4938572, rs4936443, rs57494551, rs7117261 and rs4938573. EMSAs showed a significant increase in protein binding to the risk allele of rs57494551 (p=0.0001), rs7117261 (p=0.0001) and rs4938573 (p=0.0003), but not the others, using nuclear lysates from EBV B cells. Luciferase vectors with a minimal promoter or no promoter were used to test for enhancer or promoter activity, respectively. To this end, the rs57494551 risk allele exhibited a significant increase in enhancer activity (p=0.0001). In contrast, the rs7117261 risk allele decreased enhancer activity (p=0.018). The rs4938573 risk allele decreased enhancer (p=0.043) and promoter (p=0.024) activity. While rs7117261 or rs4938573 were not reported in eQTL databases, GTex data reported rs57494551 as an eQTL that altersDDX6expression in whole blood (p=1.8E-7). Additionally, these functional SNPs have been associated with looping events to several proximal promoters in nearby genes in immune cells.Conclusion:SS and SLE have similar genomic architecture across theDDX6-CXCR5risk interval. Multiple variants in the credible set exhibited allele specific changes in protein binding, as well as modified enhancer activity, promoter activity or both. Ongoing studies will use Cas9 in EBV B cells to determine which other loci are within the local regulatory network.Disclosure of Interests:Mandi M Wiley: None declared, Bhuwan Khatri: None declared, Kandice L Tessneer: None declared, Michelle L Joachims: None declared, Anna M Stolarczyk: None declared, Astrid Rasmussen Speakers bureau: Novartis, ThermoFischer, Simon J. Bowman Consultant of: Astrazeneca, Biogen, BMS, Celgene, Medimmune, MTPharma, Novartis, Ono, UCB, xtlbio, Glapagos, Speakers bureau: Novartis, Lida Radfar: None declared, Roald Omdal: None declared, Marie Wahren-Herlenius: None declared, Blake M Warner: None declared, Torsten Witte: None declared, Roland Jonsson: None declared, Maureen Rischmueller: None declared, Patrick M Gaffney: None declared, Judith A. James Grant/research support from: Progentec Diagnostics, Inc, Consultant of: Abbvie, Novartis, Jannsen, Lars Ronnblom Grant/research support from: AZ, Speakers bureau: AZ, R Hal Scofield Grant/research support from: Pfizer, Xavier Mariette: None declared, Wan-fai Ng: None declared, Kathy L Sivils: None declared, Gunnel Nordmark: None declared, Betty Tsao: None declared, Christopher Lessard: None declared
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| 9. |
- Wiley, MM, et al.
(författare)
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SJOGREN'S DISEASE AND SYSTEMIC LUPUS ERYTHEMATOSUS DDX6-CXCR5 RISK INTERVALS REVEAL COMMON SNPS WITH FUNCTIONAL SIGNIFICANCE IN IMMUNE AND SALIVARY GLAND CELLS
- 2022
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 81, s. 269-270
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Sjögren’s Disease (SjD) and Systemic Lupus Erythematosus (SLE) are autoimmune diseases with several shared characteristics and similar genome-wide significant associations with the DDX6-CXCR5 locus. DDX6 suppresses interferon-stimulated gene expression and CXCR5 regulates T cell functions implicated in autoimmunity.ObjectivesTo identify and characterize functional SNPs in the DDX6-CXCR5 interval.MethodsImmunoChip data from European populations (3785 SLE cases; 1916 SjD cases; 6893 controls) were imputed and SNP-trait associations tested. Bayesian statistics defined a credible SNP set that was refined using bioinformatic analyses (RegulomeDB, Haploreg, ENCODE, promoter capture Hi-C, eQTLs, etc.). Electrophoretic mobility shift assays (EMSAs) and luciferase expression assays were used to test allele-specific SNP function in EBV-transformed B (EBV B) cells, Daudi B cells, Jurkat T cells, THP1 monocytes, and A253 salivary gland cell lines. Chromatin conformation capture with quantitative PCR (3C-qPCR) was used to assess long-range chromatin interactions.ResultsFine mapping of the SjD and SLE associations found similar SNP associations. Bioinformatic analyses identified 5 common SNPs with strong evidence of functionality in immune cell types: rs57494551 in an intron of DDX6, and rs4938572, rs4936443, rs7117261, and rs4938573 in the promoter/enhancer region of DDX6 and CXCR5. EMSAs and luciferase experiments showed cell type-specific differences in protein binding and promoter or enhancer activity, respectively, at each SNP. Risk allele of rs57494551 increased enhancer activity in B cells and A253 cells (p<0.001), but decreased promoter activity in T cells and A253 cells (p<0.01). SNP rs4938572 is an eQTL of DDX6 in T cells, and the risk allele significantly increased protein binding, promoter and enhancer activity in T cells (p<0.01). Risk allele of rs4938572 also increased promoter activity in A253 cells (p<0.001), but had no effect on promoter or enhancer activity in B cells. SNP rs4936443 showed no promoter or enhancer activity in immune cells, but the risk allele showed significant promoter and enhancer (p<0.001) activity in A253 cells. SNP rs7117261 showed decreased enhancer activity in EBV B cells, T cells, and A253 cells (p<0.05) and increased promoter activity in A253 cells (p<0.001). SNP rs4938573 showed decreased promoter activity in EBV B cells, T cell and A253 cells (p<0.05), decreased promoter activity in EBV B cells (p<0.05), and increased enhancer activity in A253 cells (p<0.0001). Overall, A253 cells exhibited more allele-specific effects on promoter and enhancer activity across the five SNPs compared to tested immune cells. In addition to DDX6 and CXCR5, rs57494551 and/or rs4938572 are reported eQTLs for several other genes of interest in the local chromatin regulatory network: IL10RA in T cells, TRAPPC4 in salivary gland and activated macrophages, and long non-coding (lnc)RNA AP002954.1 in T cells and whole blood. 3C-qPCR in EBV B and A253 cells showed that the two regulatory regions carrying rs4938572 or rs57494551 interacted with a region upstream of DDX6 that includes AP002954.1. Hi-C data showed looping between AP002954.1 and the regulatory region carrying rs4938572 and rs57494551 in T cells.ConclusionSjD and SLE share similar genomic architecture across the DDX6-CXCR5 risk interval with several common SNPs showing immune and salivary gland cell type-specific allelic effects on protein binding and/or enhancer/promoter activity. Extensive bioinformatic analyses suggest that the SNPs likely work within the local chromatin regulatory network to regulate cell type-specific expression of several genes on the interval. Ongoing studies will use 3C-qPCR to assess allele-specific chromatin interactions between the SNPs and these genes in different cells types, and CRISPR to determine how the risk alleles alters expression.Disclosure of InterestsMandi M Wiley: None declared, Bhuwan Khatri: None declared, Kandice L Tessneer: None declared, Michelle L Joachims: None declared, Anna M Stolarczyk: None declared, Anna Nagel: None declared, Astrid Rasmussen: None declared, Simon J. Bowman Consultant of: Abbvie, Galapagos, and Novartis in 2020-2021, Lida Radfar: None declared, Roald Omdal: None declared, Marie Wahren-Herlenius: None declared, Blake M Warner: None declared, Torsten Witte: None declared, Roland Jonsson: None declared, Maureen Rischmueller: None declared, Patrick M Gaffney: None declared, Judith A. James: None declared, Lars Ronnblom: None declared, R Hal Scofield: None declared, Xavier Mariette: None declared, Wan Fai Ng: None declared, Kathy Sivils Employee of: current employee of Janssen., Gunnel Nordmark: None declared, Betty Tsao: None declared, Christopher Lessard: None declared
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| 10. |
- Burska, A, et al.
(författare)
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TYPE I INTERFERON PATHWAY ASSAYS IN PATIENTS WITH RHEUMATIC AND MUSCULOSKELETAL DISEASES - SYSTEMATIC LITERATURE REVIEW (SLR) AND DEVELOPMENT OF CONSENSUS TERMINOLOGY FROM A EULAR TASKFORCE
- 2021
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 80, s. 415-415
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The interferon (IFN) pathway is a complex system with multiple proteins and diverse downstream effects on gene and protein expression. IFNs have been implicated in multiple RMDs. Despite significant potential, IFN assays have not progressed into clinical practice.Objectives:To perform a SLR on IFN assays in RMDs and propose a consensus terminology.Methods:OvidMedline, Embase and Web of Science were searched for reports of IFN and RMDs up to October 2019. Information about the properties of assays measuring type I IFN and measures of truth were extracted and summarised. Terminology was agreed through an interactive consensus process with reference to the existing evidence.Results:10037 abstracts were identified. 275 fulfilled eligibility criteria, and were used for data extraction. Some used more than one technique to measure IFN-I pathway activation. Hence, 275 papers generated data on 393 methods. There was great heterogeneity in the methods used and presentation of results. IFN-I pathway activation was measured using: qPCR (n=121), immunoassays (n=101), microarray (n=69), reporter cell assay (n=38), DNA methylation (n=14), flow cytometry (n=14), cytopathic effect assay (n=11), RNA sequencing (n=9), Plaque reduction assay (n=8), Nanostring (n=5), bisulphite sequencing (n=3). All papers fulfilled Face Validity. Due to lack of gold standard for IFN-I pathway activation, evidence of criterion validity was variable. Concurrent validity was presented for n=150 assays. The terminology used to describe aspects of type I IFN pathway activation was not consistent, so a consensus terminology for IFN research (Table 1) was proposed by the taskforce.Table 1.Consensus terminologyTermAbbreviationDefinitionInterferonIFNProteins with anti-viral activity; IFNs are mediators of an anti-viral response. They belong to the Type I, Type II and Type III IFN families.Type I interferonIFN-IThe IFNs alpha, beta, omega, kappa, epsilon, secreted by any nucleated cell, and binding to the IFNAR, which is expressed on any nucleated cell.Type II interferonIFN-IIIFN gamma, mostly secreted by T cells, binding to the IFNGR, which is expressed on most leucocytes.Type III interferonIFN-IIIIFN lambda, which are structurally more similar to IL-10 but share downstream signalling and gene expression with IFN-I.Interferon-stimulated genesISGsGenes whose expression is known to be upregulated by any kind of IFN. Individual ISGs may not exclusively represent Type I IFN pathway activation.Type I Interferon pathway activationAny evidence for function of the components of the Type I IFN pathway. This includes: secretion of a Type I IFN protein, binding to the IFNAR, initiation of JAK/STAT signalling pathways, expression of IFN-stimulated genes, expression of IFN-stimulated proteins.Type I interferon pathway assayAn assay measuring one or more components of the Type I IFN pathway at a molecular or functional level.Interferon stimulated gene expression signatureA qualitative description of coordinated expression of a set of ISGs that is indicative of Type I IFN pathway activation.Interferon stimulated gene expression scoreA quantitative variable derived from expression of a defined set of ISGs that is indicative of Type I IFN pathway activation.Interferon stimulated protein scoreA variable derived from expression of a defined set of soluble biomarkers known to be upregulated by IFN, although not specific for Type I IFN.InterferonopathyMonogenic diseases in which there is constitutive Type I IFN pathway activation with a causal role in pathology. The clinical picture may resemble rheumatic musculoskeletal diseases. However, most diseases with IFN pathway activation are not Interferonopathies.Conclusion:Diverse methods have been reported as IFN assays and these differ in what elements of type IFN-I pathway activation they measure. The taskforce consensus terminology on type I IFN reporting should be considered for research and clinical applications.Disclosure of Interests:Agata Burska: None declared, Javier Rodriguez Carrio: None declared, Philip G Conaghan: None declared, Willem A Dik: None declared, Robert Biesen: None declared, Maija-leena Eloranta: None declared, Giulio Cavalli: None declared, Marianne Visser: None declared, Dimitrios Boumpas: None declared, George Bertsias: None declared, Marie Wahren-Herlenius: None declared, Jan Rehwinkel: None declared, Marie-Louise Frémond: None declared, Mary K. Crow Consultant of: AstraZeneca, Bristol Meyers Squibb, Lilly, Shannon Pharmaceuticals, Grant/research support from: Gilead, Lars Ronnblom Consultant of: AstraZeneca, Edward Vital Speakers bureau: GSK, Consultant of: AURINIA, SANDOZ, GSK, AstraZeneca, Roche, Modus, Grant/research support from: AstraZeneca, Marjan Versnel: None declared
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